Back

A Pilot Study Observing High Salivary Non-Secretor (se/se) Prevalence in Breast and Ovarian Cancers in Kolkata

Das Biswas, A.; Banerjee, S.; Bhattacharya, P.

2025-09-18 oncology
10.1101/2025.09.16.25335850 medRxiv
Show abstract

BackgroundBreast and ovarian cancers remain major causes of illness and death in women. Secretor status determined by fucosyltransferase 2 (FUT2) gene controls the presence of ABO antigens in all bodily fluids except CSF, and prior studies suggest non-secretor status may be associated with cancer risk. Data on secretor prevalence in breast and ovarian cancers are limited. MethodsWe performed a pilot case-control study at a government medical college in Kolkata between January and June 2024. The study included 74 women: 37 patients with newly diagnosed breast (n = 23) or ovarian (n = 14) cancer and 37 healthy female blood donors. Unstimulated saliva was collected and tested for secretor status using a hemagglutination inhibition assay. Associations were tested with the chi-square test. ResultsAmong healthy controls, 78% were secretors and 22% were non-secretors. In contrast, 81% of cancer patients were non-secretors and 19% were secretors. By cancer type, non-secretor prevalence was 82.6% in breast cancer and 78.5% in ovarian cancer. The association between non-secretor status and cancer was statistically significant (p < 0.0001). ConclusionsIn this pilot study, salivary non-secretor status was much more common in women with breast or ovarian cancer than in healthy controls. The findings support larger, population-level and genetic studies to confirm whether FUT2 non-secretor status could serve as a simple, low-cost marker to identify the population at risk. Limitations include small sample size and absence of FUT2 genotyping.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.